Data processing system with machine learning engine to provide output generating functions

ABSTRACT

Methods, computer-readable media, and apparatuses for identifying and executing one or more interactive condition evaluation tests and collecting and analyzing social data to generate an output are provided. In some examples, user information may be received and one or more interactive condition evaluation tests may be identified. An instruction may be transmitted to a computing device of a user and executed on the computing device to enable functionality of one or more sensors that may be used in the identified tests. Upon initiating a test, data may be collected from the one or more sensors. The collected sensor data may be transmitted to the system and processed using one or more machine learning datasets. Additionally, social data may be collected and analyzed using one or more machine learning datasets to generate a social profile. The sensor data and social profile may be used together to generate an output.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of and claims priority toco-pending U.S. patent application Ser. No. 15/727,226, filed Oct. 6,2017, and entitled “Data Processing System with Machine Learning Engineto Provide Output Generating Functions,” which is a continuation of U.S.patent application Ser. No. 15/716,983, filed Sep. 27, 2017, andentitled “Data Processing System with Machine Learning Engine to ProvideOutput Generating Functions,” each of which is incorporated herein byreference in their entirety.

TECHNICAL FIELD

Aspects of the disclosure generally relate to one or more computersystems, servers, and/or other devices including hardware and/orsoftware. In particular, aspects are directed to executing interactivecondition evaluation tests and using machine learning to generate anoutput.

BACKGROUND

Mobile devices are being used to simplify people's lives around theworld. However, it is often difficult to collect sufficient informationvia user input. In addition, determining an accuracy of informationprovided by a user can be difficult. Often, confirming accuracy mayrequire in-person communication, additional documentation, and the like.Accordingly, executing a plurality of interactive tests generated by anentity to collect condition data, verify accuracy of data, and the like,may be advantageous.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of the disclosure. The summary is not anextensive overview of the disclosure. It is neither intended to identifykey or critical elements of the disclosure nor to delineate the scope ofthe disclosure. The following summary merely presents some concepts ofthe disclosure in a simplified form as a prelude to the descriptionbelow.

Aspects of the disclosure relate to methods, computer-readable media,systems, and apparatuses for identifying and executing one or moreinteractive condition evaluation tests to generate an output.

In some examples, user information may be received by a system,computing device, or the like. Based on the information, one or moreinteractive condition evaluation tests may be identified. Aninstruction, command, signal or the like, may be transmitted to acomputing device of a user and executed on the computing device toenable functionality of one or more sensors that may be used in theidentified interactive condition evaluation tests.

In some examples, a user interface may be generated by the system,computing device, or the like. The user interface may includeinstructions for executing the identified interactive conditionevaluation tests. Upon initiating an interactive condition evaluationtest on the computing device of the user, data may be collected from oneor more sensors in the computing device.

In some examples, a determination may be made as to whether a triggeringevent has occurred. If not, data from the sensors may be collected. Ifso, the interactive condition evaluation test may be terminated andfunctionality associated with the sensors may be disabled.

In some arrangements, the data collected via the sensors may betransmitted to the system, computing device, or the like, and may beprocessed using one or more machine learning datasets to generate anoutput. For instance, the data may be processed to determine aneligibility of the user, identify a product or service for the user, orthe like.

In some arrangements the test data may be used with additional data,such as social data to determine the eligibility of the user, identify aproduct or service for the user, or the like. For example, social datarelated to the user may be collected by the system from one or moredevices, from social media accounts, email accounts, etc. The socialmedia data may be scanned and analyzed to identify language or imagesproviding an indication of the user's social behavior, habits, orlifestyle choices. The social data may be analyzed using one or moremachine learning datasets to determine the user's social behavior,habits, or lifestyle choices. The analyzed social data may be used tocalculate one or more social scores related to the user and to generatea social profile based on the social scores. The social scores andsocial profile may be used together with the test data collected via thesensors to generate the output regarding the user's eligibility. Forexample, output may be generated indicating one or more premiums,discounts, incentives, or the like, for which the user is eligible. Thegenerated output may be transmitted to a computing device for display.

These and other features and advantages of the disclosure will beapparent from the additional description provided herein.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present disclosure and theadvantages thereof may be acquired by referring to the followingdescription in consideration of the accompanying drawings, in which likereference numbers indicate like features, and wherein:

FIGS. 1A and 1B illustrate an illustrative computing environment forimplementing multi-source data evaluation and control functions,according to one or more aspects described herein.

FIG. 2 illustrates an example multi-source data evaluation and controlcomputing system, according to one or more aspects described herein.

FIGS. 3A-3I depict an illustrative event sequence for performingmulti-source data evaluation and control functions, according to one ormore aspects described herein.

FIG. 4 illustrates one example flow chart illustrating an example methodof performing a multi-source data evaluation and generating an output,according to one or more aspects described herein.

FIG. 5 illustrates one example flow chart illustrating an example methodof performing a multi-source data evaluation and generating an output,according to one or more aspects described herein.

FIG. 6 illustrates one example flow chart illustrating an example methodof performing a multi-source data evaluation using additionalinteractive condition evaluation tests and generating an output,according to one or more aspects described herein.

FIG. 7 illustrates one example user interface for executing aninteractive condition evaluation test, according to one or more aspectsdescribed herein.

FIGS. 8A and 8B illustrate example user interfaces for displaying agenerated output, according to one or more aspects described herein.

FIG. 9 illustrates a network environment and computing systems that maybe used to implement aspects of the disclosure.

DETAILED DESCRIPTION

In the following description of the various embodiments, reference ismade to the accompanying drawings, which form a part hereof, and inwhich is shown by way of illustration, various embodiments of thedisclosure that may be practiced. It is to be understood that otherembodiments may be utilized.

Mobile devices are being used to perform functions that, at one time,required interaction between users, such as a customer and a vendor,service provider, or the like. However, accuracy of informationprovided, identity of a user providing input via the mobile device, andthe like, may be difficult to confirm. Accordingly, it may beadvantageous to identify and execute one or more interactive conditionevaluation tests on the mobile device to evaluate a condition of a user,determine eligibility for one or more products or services, and thelike. Additional ways of determining eligibility for one or moreproducts or services may involve analyzing social data related to a userand collected from one or more devices, social media networks, and/orother electronic sources.

In some examples, a user may request a product or service (e.g., via amobile device). The request may be transmitted to a system, computingplatform, or the like, which may process the request and transmit arequest for additional information. The user may provide the requestedadditional information via the mobile device. The additional informationmay include information such as name, age, gender, height, weight,location, email address, social media account information, authorizationto collect information, and the like.

In some arrangements, based on the information provided, one or moreproducts or services for which the user may be eligible may beidentified. Based on the identified one or more products, one or moreinteractive condition evaluation tests may be identified to determineeligibility of the user. Additionally or alternatively, social datarelated to the user may be collected and analyzed to generate a socialprofile of the user which may be used to determine, at least in part,eligibility of the user for one or more products, services, incentives,rebates, discounts, etc.

In some examples, the system may transmit an instruction to the mobiledevice to enable one or more sensors associated with the identified oneor more interactive condition evaluation tests. The one or more testsmay then be executed by the mobile device. Data from the one or moresensors may be collected during execution of the tests and may betransmitted to the system for processing. In some examples, the systemmay analyze social data collected from the user's mobile device or otherdevices, social media networks, email accounts, and other electronicsources. The system may use the analyzed social data to generate asocial profile for the user. In some arrangements, the system may usemachine learning to evaluate eligibility of the user (e.g., based on thesensor data, the analyzed social data and/or the generated socialprofile, and/or other internal and/or external data), generate an outputfor a user (e.g., a product or service to offer), and the like.

These and other aspects will be described more fully herein.

FIGS. 1A-1B depict an illustrative computing environment forimplementing and using a multi-source data evaluation and control systemin accordance with one or more aspects described herein. Referring toFIG. 1A, computing environment 100 may include one or more computingdevices and/or other computing systems. For example, computingenvironment 100 may include a multi-source data evaluation and controlcomputing platform 110, an internal data computing device 120, a firstlocal user computing device 150, a second local user computing device155, an external data computing device 140, a remote user mobilecomputing device 170, and a remote user computing device 175.

multi-source data evaluation and control computing platform 110 may beconfigured to host and/or execute one or more modules includinginstructions for providing various interactive condition evaluation testfunctions and/or factor prediction functions, one or more modulesincluding instructions for performing various social data collection andanalysis functions, and one or more modules including instructions forgenerating social profiles using analyzed social data. In some examples,the multi-source data evaluation and control computing platform 110 maybe configured to receive data from a plurality of disparate sources,aggregate the data, use a machine learning engine to generate one ormore predictions, generate and initiate one or more interactivecondition evaluation tests, generate a social profile, and the like.

One or more aspects described herein may be performed by one or moreapplications downloaded or otherwise provided to a computing device(such as first local user computing device 150, second local usercomputing device 155, remote user mobile computing device 170, remoteuser computing device 175, or the like) and executing thereon. In someexamples, the one or more applications (or portions thereof) may executein a background of the computing device.

Although various devices in the multi-source data evaluation and controlsystem are shown and described as separate devices, one or more ofmulti-source data evaluation and control computing platform 110,internal data computing device 120, external data computing device 140,first local user computing device 150, second local user computingdevice 155, remote user mobile computing device 170, and/or remote usercomputing device 175, may be part of a single computing device withoutdeparting from the disclosure.

Internal data computing device 120 may have, store and/or include dataobtained by an entity implementing the multi-source data evaluation andcontrol computing platform 110 and/or stored by the entity. In someexamples, internal data computing device 120 may include data associatedwith customers, one or more insurance claims, accident histories andassociated damages, costs, etc., user information, and the like. In someexamples, internal data computing device 120 may include multiplecomputing devices storing various different types of data. In otherexamples, internal data computing device 120 may store the various typesof data. In still other examples, internal data computing device 120 mayquery databases in one or more other computing devices, systems, or thelike, to obtain data that may be used in one or more processes describedherein.

External data computing device 140 may have, store and/or include datafrom outside of or external to the entity. For instance, external datacomputing device 140 may store or provide access to publicly availableinformation, such as weather, traffic, population, demographicinformation, and the like. Additionally or alternatively, external datacomputing device 140 may store or provide access to data related tospending habits of one or more users (e.g., types of purchases made,amounts, locations of purchases, and the like). In still other examples,external data computing device 140 may store or provide access to datarelated to behaviors of users, such as frequency of gym visits, datacollected by a wearable fitness device, and the like. In other examples,external data computing device 140 may store or provide access to socialdata related to users, such as social media posts from social networkingservices, email messages, text messages, chats, images related thereto,phone calls, contacts, social media friends and/or connections, etc. Insome examples, the external data computing device 140 is a computingdevice of an individual associated with the user (e.g., a friend,connection, relative, individual having a device connected to a deviceof the user, etc.) and participating in a service provided by themulti-source data evaluation and control system. In some examples, theexternal data computing device 140 is a computing device of an externalservice provider which collects and analyzes social data. Various othertypes of information may be accessed via the external data computingdevice 140 in some instances. In some examples, external data computingdevice 140 may access information from various sources, such as viapublic network 195.

Local user computing devices 150, 155, internal data computing system120, external data computing system 140, remote user mobile computingdevice 170, and remote user computing device 175 may be configured tocommunicate with and/or connect to one or more computing devices orsystems shown in FIG. 1A. For instance, local user computing devices150, 155 and/or internal data computing device 120 may communicate withone or more computing systems or devices via network 190, while remoteuser mobile computing device 170, remote user computing device 175,and/or external data computing device 140 may communicate with one ormore computing systems or devices via network 195. The local usercomputing devices 150, 155 and remote user computing devices 170, 175may be used to configure one or more aspects of multi-source dataevaluation and control computing platform 110, display one or morenotifications, execute one or more interactive condition evaluationtests, capture data associated with one or more interactive conditionevaluation tests, collect and analyze social data, generate a socialprofile, display outputs, and the like.

In one or more arrangements, internal data computing device 120, firstlocal user computing device 150, second local user computing device 155,external data computing device 140, remote user mobile computing device170, and/or remote user computing device 175 may be any type ofcomputing device or combination of devices capable of performing theparticular functions described herein. For example, internal datacomputing device 120, first local user computing device 150, secondlocal user computing device 155, external data computing device 140,remote user mobile computing device 170, and/or remote user computingdevice 175 may, in some instances, be and/or include server computers,desktop computers, laptop computers, tablet computers, smart phones, orthe like that may include one or more processors, memories,communication interfaces, storage devices, and/or other components. Asnoted above, and as illustrated in greater detail below, any and/or allof multi-source data evaluation and control computing platform 110,internal data computing device 120, first local user computing device150, second local user computing device 155, external data computingdevice 140, remote user mobile computing device 170, and/or remote usercomputing device 175 may, in some instances, be or includespecial-purpose computing devices configured to perform specificfunctions.

Computing environment 100 also may include one or more computingplatforms. For example, and as noted above, computing environment 100may include multi-source data evaluation and control computing platform110. As illustrated in greater detail below, multi-source dataevaluation and control computing platform 110 may include one or morecomputing devices configured to perform one or more of the functionsdescribed herein. For example, multi-source data evaluation and controlcomputing platform 110 may have or include one or more computers (e.g.,laptop computers, desktop computers, tablet computers, servers, serverblades, or the like).

As mentioned above, computing environment 100 also may include one ormore networks, which may interconnect one or more of multi-source dataevaluation and control computing platform 110, internal data computingdevice 120, first local user computing device 150, second local usercomputing device 155, external data computing device 140, remote usermobile computing device 170, and/or remote user computing device 175.For example, computing environment 100 may include private network 190and public network 195. Private network 190 and/or public network 195may include one or more sub-networks (e.g., Local Area Networks (LANs),Wide Area Networks (WANs), or the like). Private network 190 may beassociated with a particular organization (e.g., a corporation,financial institution, educational institution, governmentalinstitution, or the like) and may interconnect one or more computingdevices associated with the organization. For example, multi-source dataevaluation and control computing platform 110, internal data computingdevice 120, first local user computing device 150, and/or second localuser computing device 155, may be associated with an organization (e.g.,a financial institution), and private network 190 may be associated withand/or operated by the organization, and may include one or morenetworks (e.g., LANs, WANs, virtual private networks (VPNs), or thelike) that interconnect multi-source data evaluation and controlcomputing platform 110, internal data computing device 120, first localuser computing device 150, and/or second local user computing device155, and one or more other computing devices and/or computer systemsthat are used by, operated by, and/or otherwise associated with theorganization. Public network 195 may connect private network 190 and/orone or more computing devices connected thereto (e.g., multi-source dataevaluation and control computing platform 110, internal data computingdevice 120, first local user computing device 150, and second local usercomputing device 155) with one or more networks and/or computing devicesthat are not associated with the organization. For example, externaldata computing device 140, remote user mobile computing device 170,and/or remote user computing device 175 might not be associated with anorganization that operates private network 190 (e.g., because externaldata computing device 140, remote user mobile computing device 170, andremote user computing device 175 may be owned, operated, and/or servicedby one or more entities different from the organization that operatesprivate network 190, such as one or more customers of the organization,public or government entities, and/or vendors of the organization,rather than being owned and/or operated by the organization itself or anemployee or affiliate of the organization), and public network 195 mayinclude one or more networks (e.g., the internet) that connect externaldata computing device 140, remote user mobile computing device 170, andremote user computing device 175 to private network 190 and/or one ormore computing devices connected thereto (e.g., multi-source dataevaluation and control computing platform 110, internal data computingdevice 120, first local user computing device 150, and/or second localuser computing device 155).

Referring to FIG. 1B, multi-source data evaluation and control computingplatform 110 may include one or more processors 111, memory 112, andcommunication interfaces 113. A data bus may interconnect processor(s)111, memory 112, and communication interface(s) 113. Communicationinterface(s) 113 may be a network interface configured to supportcommunication between multi-source data evaluation and control computingplatform 110 and one or more networks (e.g., private network 190, publicnetwork 195, or the like). Memory 112 may include one or more programmodules having instructions that when executed by processor(s) 111 causemulti-source data evaluation and control computing platform 110 toperform one or more functions described herein and/or may include one ormore databases that may store and/or otherwise maintain informationwhich may be used by such program modules and/or processor(s) 111. Insome instances, the one or more program modules and/or databases may bestored by and/or maintained in different memory units of multi-sourcedata evaluation and control computing platform 110 and/or by differentcomputing devices that may form and/or otherwise make up multi-sourcedata evaluation and control computing platform 110.

For example, memory 112 may have, store, and/or include a productidentification module 112 a. The product identification module 112 a maystore instructions and/or data that may cause or enable the multi-sourcedata evaluation and control computing platform 110 to receive data from,for example, local user computing device 150, local user computingdevice 155, remote user mobile computing device 170, and/or remote usercomputing device 175 that may include a request for a product orservice, information about a user requesting the product or service, orfor whom the product or service is being requested, and the like. Insome examples, the requested product may be a life or other insuranceproduct. In some arrangements, information received may include nameand/or other identifier of a user, age, gender, height, weight, and thelike. Additional information, such as the user's email address, socialmedia account information, and authorization to collect information mayalso be received. The information may be transmitted from the local usercomputing devices 150, 155, remote user mobile computing device 170,remote user computing device 175, or the like, to the multi-source dataevaluation and control computing platform 110 and may be processed bythe product identification module 112 a to identify one or more products(e.g., a life insurance policy) to offer or recommend to the user. Insome examples, interactive tests used to determine eligibility for theone or more products may be identified based on the identified one ormore products, as will be discussed more fully herein.

Memory 112 may further have, store and/or include an interactive testidentification module 112 b. The interactive test identification module112 b may store instructions and/or data that may cause or enable themulti-source data evaluation and control computing platform 110 togenerate or identify one or more interactive condition evaluation testsbased on one or more products identified by the product identificationmodule 112 a. For instance, one or more interactive condition evaluationtests may be identified for execution by a user. The results of theidentified one or more interactive condition evaluation tests may thenbe used, either alone or in conjunction with other data, for example,social data, to determine whether a user is eligible for the one or moreproducts identified by the product identification module 112 a, a costassociated with the one or more products, a deductible associated withthe one or more products, a discount or refund that may be available tothe user if the user accepts one of the one or more products, and thelike. Some example tests may include mobility tests, cognitive skillstests, breathing or other lung capacity tests, and the like. In someexamples, the tests may be executed by the user on a mobile device, suchas remote user mobile computing device 170, or remote user computingdevice 175. Types of tests, execution of tests, and the like, will bediscussed more fully herein.

Memory 112 may further have, store and/or include a sensor activationmodule 112 c. Sensor activation module 112 c may store instructionsand/or data that may cause or enable the multi-source data evaluationand control computing platform 110 to activate or enable one or moresensors of a plurality of sensors in a user computing device, such asremote user mobile computing device 170, remote user computing device175, or the like. Some example sensors may include accelerometers,global positioning system (GPS) sensors, gyroscopes, pressure sensors,humidity sensors, pedometer, heart rate sensors, pulse sensors,breathing sensors, one or more cameras or other image capturing devices,and the like. Sensors may also include components of the computingdevice, such as a usage monitor, or the like, that may record or detectoperation of the device, applications executed, contact with a displayof the device, user input, and the like. Upon identifying, by theinteractive test identification module 112 b, one or more interactivecondition evaluation tests to be executed, the sensor activation module112 c may transmit a signal, instruction, or command to the computingdevice (e.g., remote user mobile computing device 170, remote usercomputing device 175, or the like) activating and/or enabling one ormore sensors. In some examples, the sensors activated or enabled may besensors identified for use with the identified one or more interactivecondition evaluation tests. In some arrangements, the sensors activatedor enabled may be fewer than all sensors associated with the computingdevice.

Memory 112 may further have, store, and/or include an interfacegeneration module 112 d. The interface generation module 112 d may storeinstructions and/or data that may cause or enable the multi-source dataevaluation and control computing platform 110 to generate one or moreuser interfaces associated with each identified interactive conditionevaluation test. For example, for each test identified, by theinteractive test identification module 112 b, for execution, theinterface generation module 112 d may generate one or more userinterfaces including, for example, information associated with eachtest, instructions for initiating and/or performing each test, and thelike. The interface generation module 112 d may transmit the userinterfaces to a user computing device, such as remote user mobilecomputing device 170, remote user computing device 175, or the like, andmay cause the user interface(s) to display on the device.

Memory 112 may further have, store and/or include a sensor data analysismodule 112 e. Sensor data analysis module 112 e may store instructionsand/or data that may cause or enable the multi-source data evaluationand control computing platform 110 to receive sensor data from acomputing device executing one or more interactive condition evaluationtests (e.g., remote user mobile computing device 170, remote usercomputing device 175, or the like) and analyze the sensor data. In someexamples, the sensor data analysis module 112 e may receive raw sensordata and may process the data (e.g., filter, smooth, or the like) toidentify data for analysis (e.g., data to provide the most accurateanalysis available). In some examples, one or more machine learningdatasets may be used to evaluate data from the sensor data analysismodule 112 e to evaluate a condition of the user executing the testassociated with the sensor data, as will be discussed more fully herein.In some examples, sensor data may include an outcome of a mobility test(e.g., walk a predetermined distance, walk a predetermined time on atreadmill at a designated speed, or the like), an outcome of a reflexanalysis (e.g., how quickly a user responds to a prompt on the device),an outcome of one or more cognitive skills tests (e.g., questionsdirected to evaluating memory, recognition, and the like), an outcome ofa biometric test, such as a lung capacity test (e.g., as determined froma force on which a user exhales onto the computing device from apredetermined distance), and the like.

Memory 112 may further have, store and/or include a social datacollection module 112 f. Social data collection module 112 f may storeinstructions and/or data that may cause or enable the multi-source dataevaluation and control computing platform 110 to receive social datafrom a plurality of sources, such as external data computing device 140,remote user mobile computing device 170, remote user computing device175, or other computing devices. In some examples, the social data maybe collected from a social networking service or an email server. Forinstance, social data collection module 112 f may scan one or more ofexternal data computing device 140, remote user mobile computing device170, and remote user computing device 175 to collect social dataassociated with the user. In some examples, the social data collectionmodule 112 f may further scan social media accounts and/or emailaccounts associated with the user to collect social data of the user. Insome examples, the social data collection module 112 f may further scanother devices associated with the user, such as wearable devices,fitness trackers, etc. The social data collected by the social datacollection module 112 f may include social media posts from socialnetworking services, email messages, text messages, chats, images,videos, phone calls, contacts, data identifying social media friendsand/or connections, etc. For instance, social data collection module 112f may scan various social networking service accounts associated withthe user to collect social data, such as comments, images, video, chats,messages, friends/connections, etc. posted by the user. Social datacollection module 112 f may additionally scan various social networkingservice accounts of friends, connections, relatives, individuals havingdevices connected to a device of the user, etc. to collect comments,images, video, chats, messages, friends/connections, etc. related to theuser. Further, social data collection module 112 f may scan one or moreuser computing devices associated with the user, such as remote usermobile computing device 170 or remote user computing device 175, or oneor more computing devices associated with friends, connections,relatives, individuals having devices connected to a device of the user,etc. to collect social data such as emails, text messages, images,videos, phone calls, contacts, chats, etc. stored on the device. Thesocial data may be collected with permission of the user and othersassociated with the user from whom data is collected, such as friends,connections, relatives, individuals having devices connected to a deviceof the user, etc. In this case, such friends, connections, relatives,etc. may also participate in the services provided by the multi-sourcedata evaluation and control system.

Memory 112 may further have, store and/or include a social data analysismodule 112 g. Social data analysis module 112 g may store instructionsand/or data that may cause or enable the multi-source data evaluationand control computing platform 110 to analyze the social data receivedfrom social data collection module 112 f. In some examples, the socialdata received from social data collection module 112 f may be processedand analyzed to glean a user's social behavior, habits, and lifestylechoices. For instance, the collected social data may be scanned forlanguage and images tending to reflect risky and/or positive behavior,habits, or lifestyle choices of the user. For example, images showing orposts commenting on a user smoking or skydiving may tend to reflectrisky behavior on the part of the user, while images showing or postscommenting on the user exercising or engaging in healthy eating may tendto reflect positive behavior on the part of the user. Further, researchshows that social connections and social interactions play a significantrole in an individual's physical and mental well-being and longevity.Such research shows that individuals with meaningful and emotionallyhealthy relationships are generally happier, have few health-relatedissues, and live longer lives. Accordingly, in some examples, the socialdata received from social data collection module 112 f may be processedand analyzed to glean a user's degree of social connectedness. Forinstance, the collected social data may be scanned to determine a numberof social interactions, such as a number of text messages or emails sentor received, a number of phone calls placed or received, a number ofposts made, a number of posts in which the user is referenced, a numberof photos posted, a number of photos containing images of the user, anumber of friends/connections, a number of individuals followed, anumber of followers, etc. However, research further shows that it is thequality of the social connections which counts more than the number ofsocial connections, such that even those with meaningful socialconnections and unhealthy lifestyles tend to live longer lives thanthose who have healthier lifestyles but poor social connections.Accordingly, collected social data may be further scanned to glean aquality of the user's social connections. For instance, posts containingcomments referring to the user's love or affection of a connection orposts referring the love or affection that a connection may have for theuser may tend to show that the social connection is a meaningful one. Insome examples, one or more machine learning datasets may be used toanalyze the social data received from the social data collection module112 f to glean a user's social behavior, habits, and lifestyle choices.

Memory 112 may further have, store and/or include a social profilegeneration module 112 h. Social profile generation module 112 h maystore instructions and/or data that may cause or enable the multi-sourcedata evaluation and control computing platform 110 to generate a socialprofile for the user. The social profile for the user may comprise asocial score which may be calculated by weighting differently definedcategories of analyzed social data. For example, different weightingfactors may be used to weigh different categories of analyzed socialdata. For instance, the different categories of analyzed social data mayinclude: images taken or posted by the user of the user engaging inrisky behavior, posts posted by the user describing the user engaging inrisky behavior, emails/text messages/chats sent by the user describingthe user engaging in risky behavior, images taken by another individualof the user engaging in risky behavior, posts posted by anotherindividual describing the user engaging in risky behavior, emails/textmessages/chats sent by another individual to the user describing theuser engaging in risky behavior, images taken or posted by the user ofthe user engaging in positive behavior, posts posted by the userdescribing the user engaging in positive behavior, emails/textmessages/chats sent by the user describing the user engaging in positivebehavior, images taken by another individual of the user engaging inpositive behavior, posts posted by another individual describing theuser engaging in positive behavior, emails/text messages/chats sent byanother individual to the user describing the user engaging in positivebehavior, text messages sent by the user, text messages received by theuser, emails sent by the user, emails received by the user, phone callsplaced by the user, phone calls received by the user, chats initiated bythe user, chats received by the user, posts posted by the user, posts inwhich the user is referenced, photos containing images of the user,emails/texts/chats/posts showing meaningful connections, images showingmeaningful connections, etc. Each of the different categories may have adifferent weighting factor associated with the category. For example,categories of social data related to safe behavior may have higherweighting factors than those categories related to risky behavior orthose categories indifferent to behavior. The weighting factors may bemultiplied by the number of instances of the analyzed social data foreach of the different categories of analyzed data to obtain a series ofcomponent social scores.

For example, in the case that a first weighting factor is associatedwith the category of images taken or posted by the user of the userengaging in risky behavior, the first weighting factor may be multipliedby the number of images taken or posted by the user of the user engagingin risky behavior to obtain a first component social score. As anotherexample, in the case that a second weighting factor is associated withthe category of emails received by the user, the second weighting factormay be multiplied by the number of emails received by the user to obtaina second component social score. A component social score may becalculated (e.g., by multi-source data evaluation and control dataidentifying for each category of analyzed social data. The componentsocial scores may be aggregated to determine an overall social score.The overall social score may be a numeric or non-numeric value, such asa number ranging from 1 to 100 or a letter ranging from A to F. Theoverall social score may be represented in a variety of different ways,e.g., by other numbers or letters, by words, by phrases, etc.

In some examples, the overall social scores may be categorized intogroups and ranked. As an example, an overall social score between 1 to20 may be considered a low score, an overall social score between 21 and40 may be considered a below average score, an overall social scorebetween 41 and 60 may be considered an average score, an overall socialscore between 61 and 80 may be considered an excellent score, and anoverall social score above 80 may be considered an exceptional score. Alow overall social score may reflect a higher level of risk then ahigher social score. The social profile may be comprised of thecomponent scores and/or the overall social score and may reflect adetermination of a user's risk based on the collected social data. Thesocial profile may be used as a factor in an insurance eligibilitydetermination by multi-source data evaluation and control computingplatform 110, as described below in further detail.

Memory 112 may further have, store and/or include a data aggregationmodule 112 i. Data aggregation module 112 i may store instructionsand/or data that may cause or enable the multi-source data evaluationand control computing platform 110 to receive data from a plurality ofsources. For instance, data may be received from one or more internalsources (e.g., internal data computing device 120) and/or from one ormore external sources (e.g., external data computing device 140). Thedata may include data associated with users (e.g., names, addresses,ages, genders, email addresses, social media account information,authorization to collect information, and the like), demographicinformation, locality information, behavioral information (e.g.,exercise habits, each habits, etc.), purchase habits or history, medicalinformation, social data (e.g., social media posts from socialnetworking services, email messages, text messages, chats, imagesrelated thereto, phone calls, contacts, social mediafriends/connections, etc.) and the like. Some or all of the data may becollected with permission of the user. In some examples, one or moremachine learning datasets may be used to evaluate the aggregated data,either alone or in conjunction with other data (e.g., sensor data, datafrom one or more interactive condition evaluation tests, additionalcollected social data, social scores, social profiles, or the like) todetermine one or more outputs, as will be discussed more fully herein.

Memory 112 may further have, store, and/or include a machine learningengine 112 j and machine learning datasets 112 k. Machine learningengine 112 j and machine learning datasets 112 k may store instructionsand/or data that cause or enable multi-source data evaluation andcontrol computing platform 110 to evaluate data, such as sensor data,social data, social scores, social profiles, or other data from acomputing device executing one or more interactive condition evaluationtests, aggregated data from internal sources, external sources, and thelike, to generate or determine one or more outputs (e.g., by outputgeneration module 112 l). The machine learning datasets 112 k may begenerated based on analyzed data (e.g., data from previously executedinteractive condition evaluation tests, social data, historical datafrom internal and/or external sources, and the like), raw data, and/ordata received from one or more outside sources.

The machine learning engine 112 j may receive data (e.g., social data,social scores, social profiles, data collected during one or moreinteractive condition evaluation tests executed by and received from,for example, remote user mobile computing device 170, remote usercomputing device 175, or the like, internal data computing device 120,external data computing device 140, and the like) and, using one or moremachine learning algorithms, may generate one or more machine learningdatasets 112 k. Various machine learning algorithms may be used withoutdeparting from the disclosure, such as supervised learning algorithms,unsupervised learning algorithms, regression algorithms (e.g., linearregression, logistic regression, and the like), instance basedalgorithms (e.g., learning vector quantization, locally weightedlearning, and the like), regularization algorithms (e.g., ridgeregression, least-angle regression, and the like), decision treealgorithms, Bayesian algorithms, clustering algorithms, artificialneural network algorithms, and the like. Additional or alternativemachine learning algorithms may be used without departing from thedisclosure. In some examples, the machine learning engine 112 j mayanalyze data to identify patterns of activity, sequences of activity,and the like, to generate one or more machine learning datasets 112 k.

The machine learning datasets 112 k may include machine learning datalinking one or more outcomes of an interactive condition evaluationtest, types or amounts of sensor data, historical behavioral data,transaction data, health data, social data, social scores, socialprofiles, or the like (or combinations thereof) to one or more outputs.For instance, data may be used to generate one or more machine learningdatasets 112 k linking data from interactive condition evaluation tests,internal user data, external user data, social data, social scores,social profiles, and the like, to outputs, such as a mortality rate,likelihood of developing one or more illnesses or diseases, likelihoodof risk-taking behavior, and the like. This information may be used toevaluate a risk associated with a user requesting a product or service(e.g., a life insurance product or service) to determine a premium of aninsurance policy, a discount, rebate or other incentive to offer to theuser, and the like. In some examples, the information may be used toevaluate risk associated with a user requesting an auto or home productor service (e.g., data identifying insurance product). The informationmay be used to determine a premium, deductible, incentive, or the like.

The machine learning datasets 112 k may be updated and/or validatedbased on later-received data. For instance, as additional interactivecondition evaluation tests are executed, as additional social data iscollected and social scores are calculated and social profilesgenerated, as data is collected or received from internal data computingdevice 120, external data computing device 140, and the like, themachine learning datasets 112 k may be validated and/or updated based onthe newly received information. Accordingly, the system may continuouslyrefine determinations, outputs, and the like.

The machine learning datasets 112 k may be used by, for example, anoutput generation module 112 l stored or included in memory 112. Theoutput generation module 112 l may store instructions and/or dataconfigured to cause or enable the multi-source data evaluation andcontrol computing platform 110 to generate one or more outputs based onthe machine learning dataset 112 k analysis of data (e.g., sensor data,social data, social scores, social profile, aggregate data, and thelike). For instance, as discussed above, the output generation module112 l may generate one or more premiums, discounts, incentives, or thelike, related to a product identified for a user, requested by a user,or the like. In some examples, the output generation module 112 l maytransmit the generated output to a computing device, such as remote usermobile computing device 170, remote user computing device 175, or thelike, and may cause the generated output to display on the device. Insome arrangements, the output may be transmitted to the computing devicefrom which the user requested a product, on which the one or moreinteractive condition evaluation tests were executed, or the like.

FIG. 2 is a diagram of an illustrative multi-source data evaluation andcontrol system 200 including a multi-source data evaluation and controlserver 210, an external computing device 240, a mobile device 250, andadditional related components. Each component shown in FIG. 2 may beimplemented in hardware, software, or a combination of the two.Additionally, each component of the multi-source data evaluation andcontrol system 200 may include a computing device (or system) havingsome or all of the structural components described herein for computingdevice 901 in FIG. 9 . The multi-source data evaluation and controlsystem 200 may also include or be in communication with one or morecomputing platforms, servers, devices, and the like, shown and describedwith respect to FIGS. 1A and 1B.

One or more components shown in FIG. 2 , multi-source data evaluationand control server 210, external data computing device 240, and/ormobile device 250 may communicate with each other via wireless networksor wired connections, and each may communicate with additional mobilecomputing devices, other remote user computing devices (e.g., remoteuser computing device 170) and/or a number of external computer servers,devices, etc. over one or more communication networks 230. In someexamples, the mobile computing device 250 may be paired (e.g., viaBluetooth™ technology) to one or more other devices (e.g., another userpersonal mobile computing device, such as a wearable device, tablet,etc.). If the device is no longer in proximity to be paired (e.g.,mobile computing device 250 is no longer near enough to another userpersonal mobile computing device to be paired) a notification may begenerated and displayed on the device 250 (e.g., to indicate that adevice may have been left behind).

As discussed herein, the components of multi-source data evaluation andcontrol system 200, operating individually or using communication andcollaborative interaction, may perform such features and functions suchas identifying one or more products or services, identifying one or moreinteractive condition evaluation tests, executing one or moreinteractive condition evaluation tests, collecting data associated withone or more interactive condition evaluation tests, retrieving data fromone or more internal and/or external sources, collecting social data,calculating social scores using the collected social data, generating asocial profile, generating an output, and the like.

Multi-source data evaluation and control system 200 may include one ormore mobile devices 250. Mobile device 250 may be, for example,smartphones or other mobile phones, personal digital assistants (PDAs),tablet computers, laptop computers, wearable devices such as smartwatches and fitness monitors, and the like. Mobile device 250 mayinclude some or all of the elements described herein with respect to thecomputing device 901.

Mobile device 250 may include a network interface 251, which may includevarious network interface hardware (e.g., adapters, modems, wirelesstransceivers, etc.) and software components to enable mobile device 250to communicate with multi-source data evaluation and control server 210,external computing device 240, and various other external computingdevices. One or more specialized software applications, such as testanalysis application 252 and social data analysis and social profilegeneration application 256 may be stored in the memory of the mobiledevice 250.

The test analysis application(s) 252 may be received via networkinterface 251 from the multi-source data evaluation and control server210, or other application providers (e.g., public or private applicationstores). Certain test analysis applications 252 might not include userinterface screens while other test analysis applications 252 may includeuser interface screens that support user interaction. Such test analysisapplications 252 may be configured to run as user-initiated applicationsor as background applications. The memory of mobile device 250 also mayinclude databases configured to receive and store sensor data receivedfrom mobile device sensors, usage type, application usage data, and thelike. Although aspects of the test analysis application(s) 252 aredescribed as executing on mobile device 250, in various otherimplementations, some or all of the test analysis functionalitydescribed herein may be implemented by multi-source data evaluation andcontrol server 210.

The social data analysis and social profile generation application 256may be received via network interface 251 from the multi-source dataevaluation and control server 210, or other application providers (e.g.,public or private application stores). Certain social data analysis andsocial profile generation applications 256 might not include userinterface screens while other social data analysis and social profilegeneration applications 256 may include user interface screens thatsupport user interaction. Such social data analysis and social profilegeneration applications 256 may be configured to run as user-initiatedapplications or as background applications. The memory of mobile device250 also may include databases configured to receive and store socialdata received from the mobile device 250, one or more additionaldevices, social networking services, email servers, and the like.Although aspects of the social data analysis and social profilegeneration applications 256 are described as executing on mobile device250, in various other implementations, some or all of the social dataanalysis and social profile generation functionality described hereinmay be implemented by the multi-source data evaluation and controlserver 210.

As discussed herein, mobile device 250 may include various componentsconfigured to generate and/or receive data associated with execution ofone or more interactive condition evaluation tests by or on the mobiledevice 250, and/or data associated with usage of the mobile device 250.For example, using data from sensors 253 (e.g., 1-axis, 2-axis, or3-axis accelerometers, compasses, speedometers, vibration sensors,pressure sensors, gyroscopic sensors, etc.) and/or GPS receivers orother location-based services (LBS) 254, test analysis application 252(or other device or module, e.g., multi-source data evaluation andcontrol server 210) may determine movement of the mobile device 250,evaluate actions performed with or on the mobile device 250, and thelike. The sensors 253 and/or GPS receiver or LBS component 254 of themobile device 250 may also be used to determine speeds (e.g., walkingpace, running pace, etc.), force on the mobile device 250, responsetimes for providing input to the mobile device 250, and the like.

Mobile device 250 may further include a usage monitor 255. The usagemonitor 255 may be a computing device (e.g., including a processor,computing, etc.) and may include hardware and/or software configured tomonitor various aspects of the usage of the mobile device 250. Forinstance, the usage monitor 255 may monitor a number of minutes, hours,or the like the device is in use (e.g., based on factors such as devicebeing illuminated, user interacting with or looking at the device,etc.). Further, the usage monitor 255 may monitor which applications areused above a threshold amount of time in a predetermined time period(e.g., one day, one week, one month, or the like). In still otherexamples, the usage monitor 255 may determine a type of motion or speedof motion associated with movement of the mobile device 250, whether thedevice is maintained within a case, and the like. Additional aspects ofdevice usage may be monitored without departing from the disclosure.Data related to usage of the mobile device 250 may be used to determineone or more outputs (e.g., may indicate decreased mobility, inactivelifestyle, and the like).

The mobile device 250 may be configured to establish communication withthe multi-source data evaluation and control server 210 via one or morewireless networks (e.g., network 230).

The multi-source data evaluation and control system 200 may furtherinclude an external data computing device 240. External data computingdevice 240 may store or receive data from one or more external datasources, such as user information, health information, automotiveinformation (e.g., driving behaviors, operational parameters, make,model, trim, etc.), transaction information, user behavioralinformation, social data, and the like. This information may beaggregated and processed, for instance, by multi-source data evaluationand control server 210, to generate one or more outputs. The externaldata computing device 240 may include an external data database 242 thatmay store data from one or more external sources for use in generatingone or more outputs.

The multi-source data evaluation and control system 200 also may includeone or more external servers, such as multi-source data evaluation andcontrol server 210 which may contain some or all of thehardware/software components as the computing device 901 depicted inFIG. 9 .

The multi-source data evaluation and control server 210 may include someor all of the components and/or functionality described with respect toFIGS. 1A and 1B. The multi-source data evaluation and control server 210may include one or more internal data databases 212 configured to storedata associated with, for example, data internal to the entity (e.g.,user or customer data, historical data relating to claims, accidents,social data, and the like), that may be used to evaluate risk. Further,the multi-source data evaluation and control server 210 may include testperformance analysis module 211 which may provide some or all of theoperations and/or functionality described with respect to FIGS. 1A and1B.

FIGS. 3A-3I illustrate one example event sequence for executing one ormore interactive condition evaluation tests, generating a socialprofile, and determining an output in accordance with one or moreaspects described herein. The sequence illustrated in FIGS. 3A-3I ismerely one example sequence and various other events may be included, orevents shown may be omitted, without departing from the disclosure.

With reference to FIG. 3A, in step 301, a request for a particularproduct or service, or type of product or service may be received by auser computing device, such as remote user mobile computing device 170.The request may include a request to purchase the particular product orservice. In some examples, the request may include informationassociated with a user for whom the request is made (e.g., name, contactinformation, and the like).

In step 302, the request may be transmitted from the remote user mobilecomputing device 170 to the multi-source data evaluation and controlcomputing platform 110. The request may be received by the multi-sourcedata evaluation and control computing platform 110 in step 303 and themulti-source data evaluation and control computing platform 110 mayprocess the request.

In step 304, a request for additional user information may be generated.The request may include a request for information associated with theparticular user, such as age, gender, location, occupation, tobaccousage, email address, social media account information, and the like.The request may additionally include a request for authorization tocollect social data from a user's social media accounts, email accounts,remote user mobile computing device 170, and other devices associatedwith the user, such as fitness devices, wearable devices, etc. Therequest may additionally include a request for authorization to collectsocial data from the social media accounts and devices offriends/connections of the user who also participate in the serviceprovided by the multi-source data evaluation and control system and whohave independently agreed to have social data collected from theirsocial media accounts and devices.

In step 305, the request for additional user information may betransmitted to the remote user mobile computing device 170 and, in step306, the request for additional information may be received by theremote user mobile computing device 170.

With reference to FIG. 3B, in step 307, the requested additional userinformation may be received by the remote user mobile computing device170. In step 308, the received additional user information may betransmitted to the multi-source data evaluation and control computingplatform 110.

In step 309, the received additional user information may be processedto identify one or more products or services to offer to the user thatmeet the request provided by the user (e.g., if the user has requested alife insurance policy, the multi-source data evaluation and controlcomputing platform 110 may identify one or more life insurance policiesthat may be suitable for the user based on the user information and thatmay be offered to the user).

In step 310, a request for data may be generated. For instance, themulti-source data evaluation and control computing platform 110 maygenerate one or more requests for data associated with the user. Therequests may include data related to health information of the user,spending habits or other transaction information, lifestyle information,driving behaviors, insurance claim information, social data, and thelike. The data requests may be transmitted to an external data computingdevice 140 in step 311 and/or an internal data computing device 120 instep 312. In some examples, requests for data may be transmitted toadditional computing devices. In some arrangements, the requests fordata may include a name or other unique identifier of a user that may beused as input in a query to identify the desired data.

With reference to FIG. 3C, the request for data may be received by theexternal data computing device 140 in step 313 and the internal datacomputing device 120 in step 314. In steps 315 and 316, the requesteddata may be extracted from the external data computing device 140 andinternal data computing device 120, respectively. In step 317, dataextracted from the external data computing device 140 may be transmittedto the multi-source data evaluation and control computing platform 110.In step 318, data extracted from the internal data computing device 120may be transmitted to the multi-source data evaluation and controlcomputing platform 110.

With reference to FIG. 3D, in step 319, the extracted data may bereceived and, in step 320, the extracted data may be aggregated. In someexamples, step 320 of aggregating the data may be optional.

In step 321, one or more interactive condition evaluation tests todetermine eligibility for the one or more identified products may beidentified. For instance, based on the one or more products or servicesidentified for the user, one or more interactive condition evaluationtests may be identified. In some examples, a plurality of differenttypes of interactive condition evaluation tests may be stored and, instep 321, one or more of the plurality of tests may be selected oridentified for execution on the remote user mobile computing device 170.Particular types of tests will be discussed more fully herein.

For instance, data associated with the user may be used to identify oneor more products to offer to the user and the identified one or moreproducts may be used to identify one or more interactive conditionevaluation tests to execute. In some examples, user information (e.g.,age, health information, and the like) may also be used in identifyingone or more interactive condition evaluation tests to and/or indetermining parameters of one or more interactive condition evaluationtests. For instance, if the system identifies a first test as a timedtreadmill test in which a user must walk on a treadmill for apredetermined distance (as measured by the remote user mobile computingdevice 170), the required distance may be modified based on an age of auser and/or an expected time (or time to fit into a particular category)may be modified based on the age of the user. Accordingly, in oneexample, a 65 year old user requesting life insurance may be given atest having a shorter distance or a long expected time than a 25 yearold user requesting life insurance.

In step 322, one or more interactive condition evaluation test functionsmay be initiated by the multi-source data evaluation and controlcomputing platform 110. For instance, upon identifying one or more testsfor execution, one or more functions associated with administering thetests (e.g., generating interfaces including instructions, transmittinginterfaces, processing received data, and the like) may be enabled oractivated by or within the multi-source data evaluation and controlcomputing platform 110. In some examples, upon completion of the testingprocess (e.g., upon generating an output) the enabled or activatedfunctions may be disabled or deactivated in order to conserve computingresources.

In step 323, an instruction to activate one or more sensors in theremote user mobile computing device 170 may be generated and transmittedto the remote user mobile computing device 170. For instance, uponidentifying one or more interactive condition evaluation tests forexecution by the remote user mobile computing device 170, themulti-source data evaluation and control computing platform 110 mayidentify one or more sensors within the remote user mobile computingdevice 170 that may be used to collect data associated with theidentified tests and may transmit an instruction to the remote usermobile computing device 170 to activate or enable the identifiedsensors. In step 324, the instruction may be received by the remote usermobile computing device 170 and may be executed to activate theidentified sensors.

With reference to FIG. 3E, in step 325, a user interface associated witha first test of the identified one or more interactive conditionevaluation tests may be generated. In some examples, the user interfacemay include instructions for executing the first test. In step 326, thegenerated user interface may be transmitted to the remote user mobilecomputing device 170 and, in step 327, the user interface may bedisplayed on a display of the remote user mobile computing device 170.

In step 328, the first test may be initiated and sensor data associatedwith the first test may be collected. For instance, data from one ormore sensors monitoring movement, speed, position, and the like, of theremote user mobile computing device 170 may be collected. In someexamples, data may be collected based on interaction with one or moreuser interfaces (e.g., response times, etc.). In step 329, the sensordata may be transmitted from the remote user mobile computing device 170to the multi-source data evaluation and control computing platform 110.

With reference to FIG. 3F, the sensor data may be received in step 330.In step 331, if additional tests have been identified for execution, auser interface for a second interactive condition evaluation test may begenerated. The user interface may include instructions and/or parametersfor executing the second interactive condition evaluation test by orwith the remote user mobile computing device 170.

In step 332, the user interface may be transmitted to the remote usermobile computing device 170 and, in step 333, the user interface may bedisplayed on a display of the remote user mobile computing device 170.

In step 334, sensor data associated with execution of the secondinteractive condition evaluation test may be collected and, in step 335,the collected sensor data may be transmitted to the multi-source dataevaluation and control computing platform 110.

With reference to FIG. 3G, in step 336, sensor data associated with thesecond interactive condition evaluation test may be received. In step337, the received sensor data (e.g., from the first test, second test,and any other tests) and/or other data (e.g., data from internal sources120, data from external sources 140, and the like) may be analyzed. Insome examples, analyzing the data may include comparing the data to oneor more machine learning datasets.

In step 338, social data collection may be initiated by the multi-sourcedata evaluation and control computing platform 110. That is, social datasuch as social media posts and comments from social networking services,email messages, text messages, chats, images, videos, phone calls,contacts, social media friends/connections, etc. may be collected by themulti-source data evaluation and control computing platform 110. Forinstance, the multi-source data evaluation and control computingplatform 110 may cause one or more of the internal data computing device120, the external data computing device 140, the remote user mobilecomputing device 170, or other computing device to be scanned to collectsocial data associated with the user. The multi-source data evaluationand control computing platform 110 may scan various social networkingservice accounts, email accounts, and devices associated with the userto collect the social data. The multi-source data evaluation and controlcomputing platform 110 may additionally scan various social networkingservice accounts and devices of friends, connections, relatives,individuals having devices connected to a device of the user, etc. tocollect social data related to the user. The social data may becollected with permission of the user and others associated with theuser from whom data is collected, such as friends, connections,relatives, individuals having devices connected to a device of the user,etc.

The multi-source data evaluation and control computing platform 110 maytransmit a request for social data to the external data computing device140 in step 339 and/or the remote user mobile computing device 170 instep 340. In some examples, the external data computing device 140 is acomputing device of an individual associated with the user (e.g., afriend, connection, relative, individual having a device connected to adevice of the user, etc.) and participating in a service provided by themulti-source data evaluation and control system. In some examples,requests for social data may be transmitted to additional computingdevices, such as internal data computing device 120 or other devices. Insome arrangements, the requests for the social data may include a nameor other unique identifier of a user, such as an email address or socialmedia account information, that may be used as input in a query toidentify the social data.

Referring to FIG. 3H, the request for the social data may be received bythe external data computing device 140 in step 341 and by the remoteuser mobile computing device 170 in step 342. In steps 343 and 344, thesocial data may be extracted from the external data computing device 140and remote user mobile computing device 170, respectively. In steps 345and 346, social data extracted from the external data computing device140 and the remote user mobile computing device 170, respectively, maybe transmitted to the multi-source data evaluation and control computingplatform 110. In step 347, the extracted social data may be received bythe multi-source data evaluation and control computing platform 110.

Referring to FIG. 3I, in step 348 the collected social data may beanalyzed by the multi-source data evaluation and control computingplatform 110 to glean a user's social behavior, habits, and lifestylechoices. The collected social data may be scanned for language or imageswhich may be reflective of the user's social behavior, habits, andlifestyle choices. In particular, the social data may be scanned toidentify both risky and positive behavior on the part of the user. Forexample, the social data may be scanned for certain language or certainimages which describe or show the user engaging in risky or positivebehavior. For instance, the social data may be scanned for riskybehavior such as smoking, skydiving, heavy drinking, unhealthy eating,use of illegal substances, dangerous driving, illegal activities,fighting, etc., as well as for positive behavior such as healthy eating,exercising, strong social connections, performing charity or volunteerwork, learning a foreign language, taking a class, etc. The social datamay be further scanned to determine a quantity and a quality of a user'ssocial connections and interactions. For example, to determine thequantity of social connections and interactions, the followinginformation may be collected: a number of text messages or emails sentor received, a number of phone calls placed or received, a number ofposts made, a number of posts in which the user is referenced, a numberof photos posted, a number of photos containing images of the user, etc.To determine the quality of social connections and interactions thesocial data may be further scanned to identify, for example, postscontaining comments referring to the user's love or affection of anotherindividual or posts referring the love or affection that anotherindividual may have for the user. In some examples, one or more machinelearning datasets may be used to analyze the social data to glean theuser's social behavior, habits, and lifestyle choices.

In step 349, the multi-source data evaluation and control computingplatform 110 may use the analyzed social data to generate a socialprofile for the user. The social profile may be composed of one or moresocial scores. The social scores may be calculated by weightingdifferently defined categories of the analyzed social data to determinea series of component social scores. The component scores maysubsequently be aggregated to determine an overall social score. Theanalyzed social data may be categorized into various categories, such asimages taken or posted by the user of the user engaging in riskybehavior, posts posted by the user describing the user engaging in riskybehavior, emails/text messages/chats sent by the user describing theuser engaging in risky behavior, images taken by another individual ofthe user engaging in risky behavior, posts posted by another individualdescribing the user engaging in risky behavior, emails/textmessages/chats sent by another individual to the user describing theuser engaging in risky behavior, images taken or posted by the user ofthe user engaging in positive behavior, posts posted by the userdescribing the user engaging in positive behavior, emails/textmessages/chats sent by the user describing the user engaging in positivebehavior, images taken by another individual of the user engaging inpositive behavior, posts posted by another individual describing theuser engaging in positive behavior, emails/text messages/chats sent byanother individual to the user describing the user engaging in positivebehavior, text messages sent by the user, text messages received by theuser, emails sent by the user, emails received by the user, phone callsplaced by the user, phone calls received by the user, chats initiated bythe user, chats received by the user, posts posted by the user, posts inwhich the user is referenced, photos containing images of the user,emails/texts/chats/posts showing meaningful connections, images showingmeaningful connections, etc. The number of instances for each of thedifferent categories may be determined, for example, the number of phonecalls placed by the user, the number of emails/text messages/chats sentby another individual to the user describing the user engaging in riskybehavior, the number of images showing meaningful connections, etc. Theseries of component social scores may be determined by multiplying thenumber of instances for each category of analyzed social data by aweighting factor associated with the category. The component socialscores may be aggregated to determine an overall social score, and thesocial profile may be defined by the component social scores and theoverall social score. The social profile may be used together with theanalyzed sensor and other data from step 337 to determine the user'seligibility and generate an output. For example, when one or more of thesocial scores is above a corresponding threshold value for that socialscore and one or more of the test results is above a correspondingthreshold value for that particular test, the multi-source dataevaluation and control system may determine that the user is eligiblefor one or more products, incentives, discounts, rebates, etc.

In step 350, an output may be generated based on the analysis of thesensor data and/or other data and based on the generated social profile.For instance, based on the comparison of the data to the one or moremachine learning datasets, an output may be generated. In some examples,the generated output may be a life insurance policy having parametersgenerated based on the analysis of the data. Additionally oralternatively, a premium associated with the life insurance policy mayalso be generated as an output. In still other examples, a discount,rebate or other incentive may be generated as an output. For instance,if tobacco use is detected, the multi-source data evaluation and controlsystem may generate an incentive such as a rebate if the user stopstobacco use and submits to a subsequent interactive condition evaluationtest to confirm the tobacco use has stopped. In this case, subsequentcollection of social data may also occur and may be used as anadditional method of corroborating that the user has stopped tobaccouse. For example, social data may be collected and scanned to confirmthat no images, posts, comments, etc. exist showing or describing theuser using tobacco since the date the user confirmed the tobacco use wasstopped. If images, posts, comments, etc. are found showing ordescribing the user continuing tobacco use since the date the userconfirmed the tobacco use was stopped, the multi-source data evaluationand control system may be unable to make a determination regardingwhether the user is eligible for the incentive.

Accordingly, in some examples, the multi-source data evaluation andcontrol computing platform 110 may be unable to make a determination,based on the analysis of the test data and/or the social data, regardingwhether a life insurance policy, premium, discount, incentive, etc. maybe offered to the user. This may occur, for example, when the results ofone or more of the interactive condition evaluation tests is below athreshold value, when one or more of the component or overall socialscores is below a threshold value, or when a discrepancy is foundbetween user provided information and analyzed test or social data. Insuch cases, the multi-source data evaluation and control computingplatform 110 may generate output informing the user that additionalinformation is necessary before an eligibility decision may be made. Forexample, the output may be a notification informing the user that aformal underwriting process and/or a traditional medical examination maybe necessary or that the user may need to contact the entity by phone,by mail, or via a website associated with the entity to provideadditional information prior to an eligibility decision being made.Various other outputs may be generated without departing from thedisclosure.

In step 351, the generated output may be transmitted to, for instance,the remote user mobile computing device 170. Additionally oralternatively, the generated output may be transmitted to anothercomputing device, such as first local computing device 150, second localcomputing device 155, and/or remote user computing device 175.

In step 352, the generated output may be displayed on the remote usermobile computing device 170. In some examples, displaying the generatedoutput may include an option to accept the offered product or service,identified parameters, and the like. Selection of this option may bindthe user and product or service provider. Accordingly, by executing theinteractive condition evaluation tests, collecting and analyzing socialdata, and providing results to the multi-source data evaluation andcontrol computing platform 110, the user may obtain the desired productor service without submitting to a formal underwriting process, whichmay include a physical examination, and the like.

FIG. 4 illustrates one example process for generating and evaluatinginteractive condition evaluations tests and/or other data and collectingand analyzing social data, to generate an output according to one ormore aspects described herein. The steps described with respect to FIG.4 may be performed by one or more of the various devices describedherein, such as the multi-source data evaluation and control computingplatform 110, the interactive test generation multi-source dataevaluation and control server 210, remote user mobile computing device170, and the like. In some examples, one or more of the processes orsteps described may be performed in real-time or near real-time.

In step 400, a request for a product may be received. In some examples,the request may be received from a user computing device, such as remoteuser mobile computing device 170. In step 402, user information may bereceived from, for instance, the remote user mobile computing device170. In some examples, the user information may include informationrequested by, for instance, the multi-source data evaluation and controlcomputing platform 110 and may include information such as age, gender,location, email address, social media account information, authorizationto collect information, and the like.

In step 404, one or more products and interactive tests may beidentified. For instance, the received user information may be used toidentify one or more products for which the user may be eligible andthat meet the request for the product. Based on the identified one ormore products, one or more interactive condition evaluation tests may beidentified to determine whether the user is eligible for the identifiedone or more products.

In step 406, a user interface including instructions for executing aninteractive condition evaluation test of the identified one or moreinteractive condition evaluation tests may be generated and transmittedto, for instance, the remote user mobile computing device 170. In step408, an instruction or command may be transmitted to, for instance, theremote user mobile computing device 170 to activate one or more sensorsassociated with the interactive condition evaluation test and initiatethe interactive condition evaluation test.

In step 410, data may be collected from one or more sensors, monitoringor usage devices, or the like, associated with the remote user mobilecomputing device 170. For instance, data from sensors associated withthe interactive condition evaluation test being executed may becollected and/or transmitted to the multi-source data evaluation andcontrol computing platform 110.

In step 412, a determination is made as to whether a triggering eventhas occurred. In some examples, a triggering event may include anindication that a test is complete, that one or more parameters orcriteria of the test have been met, that a threshold amount of data hasbeen received, or the like. If, in step 412, a triggering event has notoccurred, the process may return to step 410 to continue collectingdata.

If, in step 412, a triggering event has occurred, then in step 414, theinteractive condition evaluation test may be terminated, e.g., themulti-source data evaluation and control computing platform 110 maytransmit an instruction, signal, or command to terminate the test and,in some examples, disable or deactivate one or more sensors activatedfor execution of the interactive condition evaluation test.

In step 416, a determination may be made as to whether there areadditional tests identified for execution (e.g., a second or more testidentified in step 404). If so, the process may return to step 406 andmay generate and transmit instructions for a second test, etc.

If, in step 416, a determination is made that there are no additionaltests identified for execution, the collected sensor data may beprocessed in step 418. In some examples, the collected sensor data maybe processed itself. In other examples, the collected sensor data may beprocessed with other data, such as aggregated data from one or moreother sources. Processing the sensor data may include comparing thesensor data to one or more machine learning datasets to predict oridentify an output.

In step 420, social data such as social media posts and comments fromsocial networking services, email messages, text messages, chats, imagesrelated thereto, phone calls, contacts, social mediafriends/connections, etc. may be collected from one or more sources,such as the external data computing device 140, the remote user mobilecomputing device 170, or other computing device associated with the useror associated with another individual associated with the user.

In step 422 the collected social data may be analyzed to glean a user'ssocial behavior, habits, and lifestyle choices. For instance, thecollected social data may be scanned for language and images which mayindicate risky and/or positive behavior, habits, or lifestyle choices ofthe user. Analyzing the social data may include comparing the socialdata to one or more machine learning datasets to make decisionsregarding whether the scanned language and images indicate risky orpositive behavior habits.

In step 424, one or more social scores may be calculated using thecollected social data. In particular a series of component social scoresmay be calculated for each category of analyzed social data bymultiplying the number of instances for the category of analyzed socialdata by a weighted factor. The series of component social scores may beaggregated to determine an overall social score. A social score may begenerated comprising the component and overall social scores.

In step 426, an output may be generated, based on the results of theinteractive condition evaluation tests and/or the social profilegenerated from the collected social data. The output may be transmittedto and displayed, for example, via a display of remote user mobilecomputing device 170. In some examples, the output may include aninsurance product recommendation, a premium for an insurance product, adiscount or other incentive, or the like. In some examples, the outputmay include a notification that a determination regarding whether aninsurance product recommendation, a premium for an insurance product, adiscount or other incentive, etc. may be offered to the user could notbe made based solely on the results of the interactive conditionevaluation tests and/or the collected social data. In this case, theoutput may indicate that additional information is needed from the userbefore an eligibility decision may be made.

FIG. 5 illustrates one example process for aggregating data fromdisparate sources to generate an output according to one or more aspectsdescribed herein. The steps described with respect to FIG. 5 may beperformed by one or more of the various devices described herein, suchas the multi-source data evaluation and control computing platform 110,the multi-source data evaluation and control server 210, remote usermobile computing device, and the like. In some examples, one or more ofthe processes or steps described may be performed in real-time or nearreal-time.

In step 500, a request for a product may be received. In some examples,the request may be received from a user computing device, such as remoteuser mobile computing device 170. In step 502, user information may bereceived from, for instance, the remote user mobile computing device170. In some examples, the user information may include informationrequested by, for instance, the multi-source data evaluation and controlcomputing platform 110 and may include information such as age, gender,location, email address, social media account information, authorizationto collect data, and the like.

In step 504, one or more products may be identified. For instance, thereceived user information may be used to identify one or more productsfor which the user may be eligible and that meet the request for theproduct. In step 506, data may be received from a plurality of sources.For instance, data may be received from sources internal to an entityand/or sources external to an entity. For example, data may be receivedfrom one or more internal sources and may include data associated with auser, such as age, gender, location, whether the user is a homeowner,marital status, insurance history, claim history, driving behaviors, andthe like.

In some examples, data may be received from one or more external sourcesand may include data associated with the user, such asmedical/prescription history, consumer data such as transaction orpurchase history, behavioral information (e.g., gym membership, gymusage, and the like), as well as other external data, such as socialdata. For example, a source external to the multi-source data evaluationand control system may collect raw social data and process the socialdata to determine a user's social behavior, habits and lifestylechoices. In this case, the multi-source data evaluation and controlsystem may receive the raw or already processed social data from theexternal data source. In some examples, at least some data may bereceived with permission of the user.

In some examples, data received may be data associated with a computingdevice associated with the user. For instance, the multi-source dataevaluation and control computing platform 110 may receive dataassociated with movement of a user's mobile computing device, how oftenthe device is in motion, type or motion or speed (e.g., walking vs.driving), types of applications often executed on the mobile device, andthe like.

In step 508, the received data may be aggregated and, in step 510, thedata may be processed to determine whether a user is eligible for theone or more products identified. In some examples, if raw or processedsocial data is received from an external source in step 506, the socialdata may be further analyzed to determine social behavior, habits, andlifestyle choices or the user and/or to determine one or more componentand overall social scores, and to generate a social profile based on thesocial scores. In some examples, processing the data may include usingone or more machine learning datasets to determine social behavior,habits, and lifestyle choices from raw social data, determineeligibility, generate an output, and the like. In step 512, an outputmay be generated and or displayed, for instance, on a user computingdevice.

FIG. 6 illustrates one example process for renewing a product usinginteractive condition evaluation tests and collected social data,according to one or more aspects described herein. The steps describedwith respect to FIG. 6 may be performed by one or more of the variousdevices described herein, such as the multi-source data evaluation andcontrol computing platform 110, the multi-source data evaluation andcontrol server 210, remote user mobile computing device, and the like.In some examples, one or more of the processes or steps described may beperformed in real-time or near real-time.

In step 600, a binding acceptance of an offered product or generatedoutput may be received. In some examples, upon generating and displayingan output to a user, the user may have an option to select to accept anoffer associated with the output. In some arrangements, accepting theoffer may be a binding agreement and, for instance, may be performedwithout conventional underwriting processes. In step 602, based on thebinding acceptance, the product or generated output may be enabled orenacted. For instance, if the generated output is an insurance policy,acceptance of the binding offer may cause the policy to go into effect.

In step 604, a determination may be made as to whether a predeterminedtime period has elapsed. For example, the selected product or output maybe enacted for a predetermined time period or term. Upon expiration ofthat term, the product may be cancelled if it is not renewed.Accordingly, in advance of the product being cancelled, and after apredetermined time (e.g., a predetermined time less than the term of theproduct), the system may offer the user an option to renew. Accordingly,the system may determine whether the predetermined time period less thanthe term of the product has elapsed. If not, the product may remainenabled or enacted in step 606.

If, in step 604, the time period has elapsed, the user may renew theproduct. In step 608, the user may be authenticated to the system. Forinstance, a notification may be transmitted to the user requesting theuser to login to the system for renewal. In some examples, logging infor renewal may include determining whether user authenticatingcredentials match pre-stored user authenticating credentials. In someexamples, credentials may include username and password, biometric datasuch as fingerprint, iris scan, facial recognition, and the like.

In step 610, one or more interactive condition evaluation tests may beidentified to determine whether the user is eligible to renew, one ormore parameters of the renewal may be identified, and the like. Similarto other aspects described herein, the interactive condition evaluationtests may be identified based on user information, a current product,and the like.

In step 612, the additional tests may be executed. Similar to otherarrangements described herein, the additional tests may be executed viaa computing device of the user (e.g., remote user mobile computingdevice 170). In step 614, test data may be collected from test executionand may be processed, for instance, using one or more machine learningdatasets.

In step 616, social data related to the user may be collected from oneor more data sources. The collected social date may be analyzed in step618 to glean social behaviors, habits, and lifestyle choices of theuser. In step 620 the analyzed social data may be used to calculatecomponent and overall social scores which may be used to generate asocial profile for the user.

In step 622, based on the processed test data and the generated socialprofile, an output may be generated and displayed to the user. In someexamples, the output may include an offer or recommendation to maintainor renew the product currently enabled or to modify the product (e.g.,obtain a different product, modify one or more parameters of theproduct, and the like) or may be a notification indicating thatadditional information is needed before a multi-source data evaluationmay be made regarding an offer or recommendation to maintain, renew, ormodify the product.

FIG. 7 illustrates one example user interface that may be generated andtransmitted to a mobile device of a user. The user interface 700 mayinclude identification of a first test, instructions for performing thefirst test, and the like. The user may initiate the test by selecting“GO” or other option.

FIGS. 8A and 8B illustrate example user interfaces providing a generatedoutput. Referring to FIG. 8A, the interface 800 a may include anindication of the product for which the user is eligible or the productbeing offered, as well as a cost associated with the product. In someexamples, a link may be provided to additional information, parameters,term, conditions, and the like. The interface 800 a may further includean option to accept the offer. Acceptance of the offer may bind the userin real-time, in at least some examples.

Referring to FIG. 8B, the interface 800 b may include a notificationindicating that additional information is necessary prior to amulti-source data evaluation being made. In some examples, a link may beprovided to provide the user with additional details about theadditional information needed to proceed with determining eligibility.

FIG. 9 illustrates a block diagram of a computing device (or system) 901in a computer system 900 that may be used according to one or moreillustrative embodiments of the disclosure. The computing device 901 mayhave a processor 903 for controlling overall operation of the computingdevice 901 and its associated components, including RAM 905, ROM 907,input/output module 909, and memory 915. The computing device 901, alongwith one or more additional devices (e.g., terminals 950 and 951,security and integration hardware 960) may correspond to any of multiplesystems or devices, such as a user personal mobile computing device,computing platform, or a computer server, configured as described hereinfor collecting data, identifying and executing one or more interactivecondition evaluation tests, evaluating data, generating a socialprofile, generating outputs, and the like.

Input/Output (I/O) 909 may include a microphone, keypad, touch screen,and/or stylus through which a user of the computing device 901 mayprovide input, and may also include one or more of a speaker forproviding audio output and a video display device for providing textual,audiovisual and/or graphical output. Software may be stored withinmemory 915 and/or storage to provide instructions to processor 903 forenabling computing device 901 to perform various actions. For example,memory 915 may store software used by the computing device 901, such asan operating system 917, application programs 919, and an associatedinternal database 921. The various hardware memory units in memory 915may include volatile and nonvolatile, removable and non-removable mediaimplemented in any method or technology for storage of information suchas computer readable instructions, data structures, program modules orother data. Certain devices/systems within the multi-source dataevaluation and control system may have minimum hardware requirements inorder to support sufficient storage capacity, analysis capacity, networkcommunication, etc. For instance, in some embodiments, one or morenonvolatile hardware memory units having a minimum size (e.g., at least1 gigabyte (GB), 2 GB, 5 GB, etc.), and/or one or more volatile hardwarememory units having a minimum size (e.g., 256 megabytes (MB), 512 MB, 1GB, etc.) may be used in a device 901 (e.g., a mobile computing device901, multi-source data evaluation and control server 901, externalserver 901, etc.), in order to store and execute multi-source dataevaluation and control software application, execute tests, collect andanalyze data, generate a social profile, generate outputs, generaterecommendations and/or incentives, etc. Memory 915 also may include oneor more physical persistent memory devices and/or one or morenon-persistent memory devices. Memory 915 may include, but is notlimited to, random access memory (RAM) 905, read only memory (ROM) 907,electronically erasable programmable read only memory (EEPROM), flashmemory or other memory technology, CD-ROM, digital versatile disks (DVD)or other optical disk storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othermedium that can be used to store the desired information and that can beaccessed by processor 903.

Processor 903 may include a single central processing unit (CPU), whichmay be a single-core or multi-core processor (e.g., dual-core,quad-core, etc.), or may include multiple CPUs. Processor(s) 903 mayhave various bit sizes (e.g., 16-bit, 32-bit, 64-bit, 96-bit, 128-bit,etc.) and various processor speeds (ranging from 100 MHz to 5 Ghz orfaster). Processor(s) 903 and its associated components may allow thesystem 901 to execute a series of computer-readable instructions, forexample, to identify interactive condition evaluation tests, executingtests, collecting and analyzing data, generating outputs, and the like.

The computing device (e.g., a mobile computing device, computingplatform, server, external server, etc.) may operate in a networkedenvironment 900 supporting connections to one or more remote computers,such as terminals 950 and 951. The terminals 950 and 951 may be personalcomputers, servers (e.g., web servers, database servers), or mobilecommunication devices (e.g., mobile phones, portable computing devices,on-board vehicle-based computing systems, and the like), and may includesome or all of the elements described above with respect to thecomputing device 901. The network connections depicted in FIG. 9 includea local area network (LAN) 925 and a wide area network (WAN) 929, and awireless telecommunications network 933, but may also include othernetworks. When used in a LAN networking environment, the computingdevice 901 may be connected to the LAN 925 through a network interfaceor adapter 923. When used in a WAN networking environment, the device901 may include a modem 927 or other means for establishingcommunications over the WAN 929, such as network 931 (e.g., theInternet). When used in a wireless telecommunications network 933, thedevice 901 may include one or more transceivers, digital signalprocessors, and additional circuitry and software for communicating withwireless computing devices 940 (e.g., mobile phones, portable computingdevices, on-board vehicle-based computing systems, etc.) via one or morenetwork devices 935 (e.g., base transceiver stations) in the wirelessnetwork 933.

Also illustrated in FIG. 9 is a security and integration layer 960,through which communications may be sent and managed between the device901 (e.g., a user's personal mobile device, an multi-source dataevaluation and control computing platform or server, etc.) and theremote devices (950 and 951) and remote networks (925, 929, and 933).The security and integration layer 960 may comprise one or more separatecomputing devices, such as web servers, authentication servers, and/orvarious networking components (e.g., firewalls, routers, gateways, loadbalancers, etc.), having some or all of the elements described abovewith respect to the computing device 901. As an example, a security andintegration layer 960 of a mobile computing device, computing platform,or a server operated by an insurance provider, financial institution,governmental entity, or other organization, may comprise a set of webapplication servers configured to use secure protocols and to insulatethe server 901 from external devices 950 and 951. In some cases, thesecurity and integration layer 960 may correspond to a set of dedicatedhardware and/or software operating at the same physical location andunder the control of same entities as driving data analysis server 901.For example, layer 960 may correspond to one or more dedicated webservers and network hardware in an organizational datacenter or in acloud infrastructure supporting a cloud-based driving data analysissystem. In other examples, the security and integration layer 960 maycorrespond to separate hardware and software components which may beoperated at a separate physical location and/or by a separate entity.

As discussed below, the data transferred to and from various devices inthe system 900 may include secure and sensitive data, such as deviceusage data, application usage data, medical or personal information,test result data, and the like. Therefore, it may be desirable toprotect transmissions of such data by using secure network protocols andencryption, and also to protect the integrity of the data when stored onin a database or other storage in a mobile device, multi-source dataevaluation and control computing platform, or server and other computingdevices in the system 900, by using the security and integration layer960 to authenticate users and restrict access to unknown or unauthorizedusers. In various implementations, security and integration layer 960may provide, for example, a file-based integration scheme or aservice-based integration scheme for transmitting data between thevarious devices in the system 900. Data may be transmitted through thesecurity and integration layer 960, using various network communicationprotocols. Secure data transmission protocols and/or encryption may beused in file transfers to protect to integrity of the driving data, forexample, File Transfer Protocol (FTP), Secure File Transfer Protocol(SFTP), and/or Pretty Good Privacy (PGP) encryption. In other examples,one or more web services may be implemented within the various devices901 in the system 900 and/or the security and integration layer 960. Theweb services may be accessed by authorized external devices and users tosupport input, extraction, and manipulation of the data (e.g., deviceusage data, location data, vehicle data, etc.) between the variousdevices 901 in the system 900. Web services built to support system 900may be cross-domain and/or cross-platform, and may be built forenterprise use. Such web services may be developed in accordance withvarious web service standards, such as the Web Service Interoperability(WS-I) guidelines. In some examples, a movement data and/or driving dataweb service may be implemented in the security and integration layer 960using the Secure Sockets Layer (SSL) or Transport Layer Security (TLS)protocol to provide secure connections between servers 901 and variousclients 950 and 951 (e.g., mobile devices, data analysis servers, etc.).SSL or TLS may use HTTP or HTTPS to provide authentication andconfidentiality. In other examples, such web services may be implementedusing the WS-Security standard, which provides for secure SOAP messagesusing XML, encryption. In still other examples, the security andintegration layer 960 may include specialized hardware for providingsecure web services. For example, secure network appliances in thesecurity and integration layer 960 may include built-in features such ashardware-accelerated SSL and HTTPS, WS-Security, and firewalls. Suchspecialized hardware may be installed and configured in the security andintegration layer 960 in front of the web servers, so that any externaldevices may communicate directly with the specialized hardware.

Although not shown in FIG. 9 , various elements within memory 915 orother components in the system 900, may include one or more caches, forexample, CPU caches used by the processing unit 903, page caches used bythe operating system 917, disk caches of a hard drive, and/or databasecaches used to cache content from database 921. For embodimentsincluding a CPU cache, the CPU cache may be used by one or moreprocessors in the processing unit 903 to reduce memory latency andaccess time. In such examples, a processor 903 may retrieve data from orwrite data to the CPU cache rather than reading/writing to memory 915,which may improve the speed of these operations. In some examples, adatabase cache may be created in which certain data from a database 921(e.g., interactive condition evaluation test result database, internaldata database, external data database, etc.) is cached in a separatesmaller database on an application server separate from the databaseserver. For instance, in a multi-tiered application, a database cache onan application server can reduce data retrieval and data manipulationtime by not needing to communicate over a network with a back-enddatabase server. These types of caches and others may be included invarious embodiments, and may provide potential advantages in certainimplementations of performing functions describes herein.

It will be appreciated that the network connections shown areillustrative and other means of establishing a communications linkbetween the computers may be used. The existence of any of variousnetwork protocols such as TCP/IP, Ethernet, FTP, HTTP and the like, andof various wireless communication technologies such as GSM, CDMA, WiFi,and WiMAX, is presumed, and the various computer devices and systemcomponents described herein may be configured to communicate using anyof these network protocols or technologies.

Additionally, one or more application programs 919 may be used by thevarious computing devices 901 within the system 900 (e.g., softwareapplications, etc.), including computer executable instructions foridentifying one or more products, identifying one or more interactivecondition evaluation tests, executing interactive condition evaluationtests, collecting data, analyzing data, and the like, as describedherein.

As discussed herein, various examples for generating an output based ondifferent types of data from different sources are described. In someexamples, machine learning may be used to generate one or more outputs.Using data from various different sources, as well as different types ofdata, may provide more accurate predictions of risk, mortality, and thelike, in order to generate and offer outputs that are more closelytailored to a user's needs.

Further, using the various types of data, as well as machine learning,may allow an entity generating an output to better align pricing with adetermined risk. In conventional systems, it may take several years toevaluate outputs, such as a determined risk for a particular user, apredicted mortality, or the like. By processing great volumes of data togenerate machine learning datasets, validation of risk predictions orassumptions, and the like may be performed much more quickly whichultimately may allow for pricing of products (e.g., insurance policies,and the like) at a more granular level.

As discussed, one or more interactive condition evaluation tests may beused to collect data associated with a user, assess conditions of theuser, the resulting test data, together with other data, such as socialdata, may be used to determine whether a user is eligible for a product,and/or to generate an output (e.g., an insurance policy to offer, apremium for an insurance policy, a discount or incentive, or the like).Below are several example interactive condition evaluation tests thatmay be used with one or more arrangements described herein. Variousother tests may be used without departing from the disclosure andnothing in the examples below should be viewed as limiting theinteractive condition evaluation tests to only these examples.

In one example, an interactive condition evaluation test may includeevaluating mobility of a user. Accordingly, the multi-source dataevaluation and control computing platform 110 may generate a userinterface including instructions for executing a mobility test using amobile device of the user. The user may receive the interface which maybe displayed on the mobile device. In some examples, the test mayinclude instructing a user to walk, run, jog, or the like, apredetermined distance. Sensors within the mobile device may track thedistance walked, time for walking the distance, pace of the user, andthe like. In some examples, data related to heart rate of the user,pulse of the user, and the like, may also be collected by one or moresensors in the mobile device. This information may then be transmittedto the multi-source data evaluation and control computing platform 110for processing and analysis.

In another example, a user may be instructed to walk, run, jog, or thelike, on a treadmill for a predetermined time, at a predetermined pace,or the like, while carrying the user's mobile device. Sensors within thedevice may detect and/or collect data associated with performance of thetest, heart rate, pulse, and the like, and this information may betransmitted to the multi-source data evaluation and control computingplatform 110 for processing and analysis.

In some arrangements, for either of the above-described exampleinteractive tests, video may be captured of the user while performingthe test. This video may be further evaluated to determine a gait ofuser, how easily the user managed the interactive test, or the like.

In other example interactive tests, a user may be instructed to performone or more other physical functions (e.g., outside of walking, runningor the like). For instance, a user may be requested to hold his or herarms in front of his or her body for as long as possible while holdingthe mobile device. One or more sensors within the mobile device maycollect data associated with a position of the mobile device, time in aparticular position, and the like, and this information may betransmitted to the multi-source data evaluation and control computingplatform 110 for processing and analysis.

In some examples, similar physical tests may be performed with a user'slegs (e.g., sit in chair and extend legs).

In some examples, one or more interactive tests may test a reflex of auser. For instance, an image may be displayed on a mobile device of auser with instructions to touch one or more icons indicating a certainitem (e.g., a plurality of icons are displayed, touch or select all thatare a particular object). The sensors and/or other mobile devicecomponents may detect not only how many correct answers the userprovided but also how quickly the user was able to respond (e.g., howquickly the user could touch the screen). This data may then betransmitted to the multi-source data evaluation and control computingplatform 110 for processing an analysis.

In another example interactive condition evaluation test for reflexes,the user may be instructed to touch a display of the mobile device asquickly as possible upon seeing a particular prompt. The mobile devicemay then collect data associated with how quickly the user touched thedisplay and may transmit that data for processing and analysis.

Additional interactive condition evaluation tests may be directed toevaluating a user's recall. For instance, a user may be provided with alist of words that they may view for a predetermined time period. Afterthe time period expires, the user may be requested to input as manywords as he or she can remember. The words may be input via a keyboard(e.g., virtual or physical) or spoken.

In some examples, one or more interactive condition evaluation tests maybe used to evaluate a lung capacity or respiration of a user. Forinstance, a tobacco user may have a reduced lung capacity, increasedrespiration rate, or the like. Accordingly, one or more interactivecondition evaluation tests may include having a user exhale onto amobile device and one or more sensors may be detect a number ofexhalations, a velocity of the breath, a rate of exhalations, and thelike. In some examples, the user may exhale onto a microphone of themobile device and the audio received may be processed to determine astrength of exhale, number of exhalations, and the like. In someexamples, one or more test may request a user to exhale for apredetermined amount of time while positioned a predetermined distancefrom the mobile device. This information may be transmitted to themulti-source data evaluation and control computing platform 110 forprocessing and analysis.

In some examples, one or more interactive condition evaluation tests mayinclude monitoring sleep habits of a user. This data may then betransmitted for processing and analysis.

In some examples, one or more interactive condition evaluation tests mayincluding requesting a user to capture one or more images of particularbody parts, or the like. For instance, images of the user may be used todetermine height, weight, overall health appearance, and the like. Insome examples, the user may be requested to submit particular images.For instance, a close up image of an eye of a user may be used todetermine one or more health issues, such as coronary disease,hypertension, diabetes, and the like.

In some examples, the system may generate a plurality of tests forexecution. A user may, in some examples, complete some or all of thetests. If the user completes fewer than all of the tests, the outputgenerated may be impacted by completion of fewer than all of theidentified tests (e.g., output may include a higher premium for a policythan a user completing all tests, discount or incentive may be differentfrom a user who completed all tests, or the like).

Although various aspects described herein are described as beingexecuted by a mobile device of a user, a mobile device may, in someexamples, include a wearable device, such as a fitness tracker. One ormore tests may be executed via the fitness tracker, data may becollected and transmitted, and the like. In some examples, data from afitness tracker or other wearable device may be used in combination withother data (e.g., may be used as data from an external source,collected, aggregated and processed, as discussed herein).

Data from sources other than the interactive condition evaluation testsmay also be used, as discussed herein. For instance, data from internalsources and/or external sources may be used to evaluate risk, generateoutputs, provide offers, and the like.

For instance, in some examples, data associated with usage of a mobiledevice may be collected and used in analyzing eligibility, generatingoutputs, and the like. For instance, types of applications accessed by auser, how often applications are accessed, and the like, may becollected and used in the analysis. For example, if a user executes oneor more health or fitness applications on a mobile device, that mayindicate a healthy lifestyle. Alternatively, if the mobile device isoften used for streaming video, that may indicated a more sedentarylifestyle. These factors may be used to evaluate eligibility, determinean output, or the like.

As discussed herein, various types of internal data may be collected andused in making various output determinations. For instance, if theentity implementing the system is an insurance provider, data associatedwith home insurance, auto insurance, life insurance, and the like may beused. In some examples, historical data such as claims data, and thelike, may be used in generating one or more machine learning datasets.Data associated with a particular user requesting a product may also beextracted and used to generate an output. For example, user claimhistory, vehicle operational data or driving behaviors (e.g., ascollected from a vehicle of the user, mobile device of the user, or thelike), may be used.

As also discussed herein, various types of external data may becollected and used in making various output determinations. In someexamples, the external data may be received from one or more sourcesexternal to an entity implementing the system. The external sources mayinclude publicly available information, anonymous information,information collected with permission of the user, and the like. Someexamples of external data are provided below. However, various othertypes of external data may be collected and used without departing fromthe disclosure and the examples below should not be viewed as limitingexternal data to only these types of data.

In some examples, consumer data such as transaction data and the likemay be used. For instance, data collected via a loyalty program atgrocery stores, department stores, and the like, may be used to evaluatea lifestyle of user. Data such as types of purchases made, locations ofpurchase, frequency of purchase, amount of purchase, and the like may beconsidered. In some examples, purchases made at a grocery store (e.g.,healthy foods, cigarettes, alcohol, or the like) may be collected andevaluated to generate one or more outputs.

In some examples, external data such as medical information of the usermay be collected and used in the analysis. This data may be collectedwith permission of the user and may include prescriptions used, medicaldiagnosis, recent lab results, recent results of a physical examination,family medical history, electronic health records, and the like.

In some arrangements, other behavioral data may be used. For instance,whether a user has a membership to a gym, how often the user visits thegym, and the like, may be used. In some examples, global positioningsystem data may be used to determine or verify a position of a user(e.g., user visits a gym 5 days/week). Additionally or alternatively,detecting behaviors such as marathon running, 5K running, or the like,may be detected from sensor data, as well as time, pace, and the like.This data may be collected and used in evaluation for generatingoutputs.

Data associated with occupation and/or hobbies may also be considered.For instance, detection of, for instance, skydiving, as a hobby (e.g.,based on altimeter sensor data from a mobile device) may indicate a riskfactor for a user. In some examples, data associated with an occupationmay be collected. For instance, detection of frequent changes inaltitude, speed, and the like, may indicate a user is a flightattendant, pilot, or the like. This information may be used inevaluation.

In some examples, social data may be collected, analyzed, and used toevaluate risk, generate outputs, provide offers, and the like, asdescribed herein. The collected social data may further be used toidentify discrepancies in information provided by the user. For example,in addition to being used to make eligibility determinations, collectedsocial data may be used to identify any discrepancies that may exist ininformation provided by a user, such as on an application provided bythe user to the entity. For example, if a user has indicated on anapplication that the user is a non-smoker and social data collected forthe user describes or shows the user smoking, such information may beused to identify a discrepancy in the application. In such situations,the system may determine that a formal underwriting process must beundertaken or that additional information must be provided before aneligibility determination may be made.

In some examples, user data may be collected over a period of time todetermine how sedentary a life a user lives. For instance, the movementof the mobile device may be tracked via one or more sensors and thatinformation may be transmitted for processing and analysis. In someexamples, this data may be collected during an eligibility evaluationprocess (e.g., before an output is generated, an offer is provided, orthe like). Additionally or alternatively, the data may be collectedduring a term of, for instance, an insurance policy, to monitor a user'slifestyle. In some examples, historical data from a time prior to theuser requesting a product may be collected and evaluated to identifypotential risk. Data may also be collected after the user has purchasedthe product to continue to evaluate risk. This continuous or continuedcollection may be also be used for dynamic pricing (e.g., pricing thatmay change based on detected behaviors) and/or for renewal of a product.

As discussed herein, in some examples, a user may accept a generatedoutput or offer and a binding agreement may be made. In somearrangements, one or more of the data collection, processing, offer andacceptance may be performed in real-time. In some examples, the bindingagreement may be based solely on the data collected from interactivecondition evaluation tests, collected social data, a generated socialprofile, internal data, external data, and the like (e.g., withouttraditional underwriting, physical examination or the like). In otherexamples, a user may be provided with an output having a first price.Acceptance of the offer may include the user agreeing to the firstprice, however, an incentive may be generated for a user to provideadditional information, such as recent medical examination results, labwork, or the like. Accordingly, a rebate, refund, credit, or the like,may be offered for providing this additional information.

In some examples, a user may also permit an entity to use the collecteddata, generated outputs, test results, and the like in determiningeligibility for one or more other products. For instance, a system maygenerate a recommended other product (e.g., long term care insurance,auto insurance, or the like) and the data collected may be used toevaluate risk, eligibility, and the like. In some examples, the data maybe used to evaluate requests made by the user for additional products.

As discussed above, biometric data such as fingerprints and the like,and/or facial recognition data may be used to authenticate a user,provide additional functionality, and the like. For instance, uponinitiating an interactive condition evaluation test, a user may berequested to capture an image of himself or herself. Facial recognitionmay then be used to confirm that the image captured corresponds to theuser. In some examples, public records may be used to confirm thisinformation. In other examples, the user may be asked to provide animage of, for instance, a driver's license. This may then be compared toa captured image to verify the identity of the user.

In some arrangements, fingerprint or other biometric data may also beused. For instance, a user may submit a fingerprint with acceptance ofan offer, for an insurance policy or the like. If a claim is then madeagainst the policy, or a modification is requested, the user mayauthenticate by submitting a fingerprint.

In another example, a beneficiary of an insurance policy may beidentified by his or her fingerprint. Accordingly, the beneficiary maysubmit the fingerprint upon a user purchasing the policy. Thebeneficiary may then submit a fingerprint to submit a claim.

In some arrangements, one or more aspects described herein may beembodied in an application executing on a computing device of a user. Insome arrangements, upon opening the application, various functionalitymay be enabled. For instance, sensors may be activated, permission maybe given to collect data, and the like. Although various aspectsdescribed herein are described with respect to life insurance policies,one or more aspects described herein may be used to evaluate eligibilityfor other products or services, such as auto insurance, homeownersinsurance, long term care insurance, and the like.

One or more aspects of the disclosure may be embodied in computer-usabledata or computer-executable instructions, such as in one or more programmodules, executed by one or more computers or other devices to performthe operations described herein. Generally, program modules includeroutines, programs, objects, components, data structures, and the likethat perform particular tasks or implement particular abstract datatypes when executed by one or more processors in a computer or otherdata processing device. The computer-executable instructions may bestored as computer-readable instructions on a computer-readable mediumsuch as a hard disk, optical disk, removable storage media, solid-statememory, RAM, and the like. The functionality of the program modules maybe combined or distributed as desired in various embodiments. Inaddition, the functionality may be embodied in whole or in part infirmware or hardware equivalents, such as integrated circuits,Application-Specific Integrated Circuits (ASICs), Field ProgrammableGate Arrays (FPGA), and the like. Particular data structures may be usedto more effectively implement one or more aspects of the disclosure, andsuch data structures are contemplated to be within the scope of computerexecutable instructions and computer-usable data described herein.

Various aspects described herein may be embodied as a method, anapparatus, or as one or more computer-readable media storingcomputer-executable instructions. Accordingly, those aspects may takethe form of an entirely hardware embodiment, an entirely softwareembodiment, an entirely firmware embodiment, or an embodiment combiningsoftware, hardware, and firmware aspects in any combination.Furthermore, such aspects may take the form of a computer programproduct stored by one or more computer-readable storage media havingcomputer-readable program code, or instructions, embodied in or on thestorage media. In addition, various signals representing data or eventsas described herein may be transferred between a source and adestination in the form of light or electromagnetic waves travelingthrough signal-conducting media such as metal wires, optical fibers, orwireless transmission media (e.g., air or space). In general, the one ormore computer-readable media may be and/or include one or morenon-transitory computer-readable media.

As described herein, the various methods and acts may be operativeacross one or more computing servers and one or more networks. Thefunctionality may be distributed in any manner, or may be located in asingle computing device (e.g., a server, a client computer, and thelike). For example, in alternative embodiments, one or more of thecomputing platforms discussed above may be combined into a singlecomputing platform, and the various functions of each computing platformmay be performed by the single computing platform. In such arrangements,any and/or all of the above-discussed communications between computingplatforms may correspond to data being accessed, moved, modified,updated, and/or otherwise used by the single computing platform.Additionally or alternatively, one or more of the computing platformsdiscussed above may be implemented in one or more virtual machines thatare provided by one or more physical computing devices. In sucharrangements, the various functions of each computing platform may beperformed by the one or more virtual machines, and any and/or all of theabove-discussed communications between computing platforms maycorrespond to data being accessed, moved, modified, updated, and/orotherwise used by the one or more virtual machines.

Aspects of the disclosure have been described in terms of illustrativeembodiments thereof. Numerous other embodiments, modifications, andvariations within the scope and spirit of the appended claims will occurto persons of ordinary skill in the art from a review of thisdisclosure. For example, one or more of the steps depicted in theillustrative figures may be performed in other than the recited order,one or more steps described with respect to one figure may be used incombination with one or more steps described with respect to anotherfigure, and/or one or more depicted steps may be optional in accordancewith aspects of the disclosure.

What is claimed is:
 1. A method, comprising: receiving, at acommunication interface of an eligibility determination computingplatform and from a user computing device, a user input including userinformation for a user, wherein the user input includes sensorinformation collected by sensors of the user computing device;identifying, via a product identification module of the eligibilitydetermination computing platform and based on the received userinformation, one or more products; identifying, via a social dataanalysis module of the eligibility determination computing platform, oneor more social networking service accounts associated with the user;collecting, by a social data collection module of the eligibilitydetermination computing platform, social data related to the user byscanning the one or more social networking service accounts associatedwith the user; analyzing, by the social data analysis module of theeligibility determination computing platform and using one or moremachine learning datasets generated using a machine learning engine, thesocial data to evaluate social habits of the user; determining aquantity of the social data falling into each of a plurality ofcategories of social behavior; calculating, via a social profilegeneration module of the eligibility determination computing platformand based on the analysis of the social data via the one or more machinelearning datasets and the quantity of social data falling into each ofthe plurality of categories of social behavior, one or more socialscores; determining, based on the one or more social scores, whether theuser is eligible for the identified one or more products; and sending,via the communication interface and to the user computing device, anoutput indicating whether the user is eligible for the identified one ormore products.
 2. The method of claim 1, wherein the social datacollected from the one or more social networking service accountscomprises social media posts associated with the user, social mediamessages associated with the user, and a list of social media friends orconnections associated with the user, and wherein the social media postscomprise one or more of: a message, an image, a video, or an audio. 3.The method of claim 1, wherein analyzing the social data to evaluate thesocial habits of the user comprises: scanning the social data forlanguage or images indicative of social behavior of the user; andanalyzing, using the one or more machine learning datasets, the languageor images to evaluate the social habits of the user.
 4. The method ofclaim 1, wherein calculating the one or more social scores comprisescalculating the one or more social scores based on weighting differentlyeach of the plurality of categories of social behavior.
 5. The method ofclaim 4, wherein the plurality of categories of social behaviorcomprise: a number of images showing the user engaging in riskybehavior, a number of social media posts describing the user engaging inrisky behavior, a number of images showing the user engaging in positivebehavior, a number of social media posts describing the user engaging inpositive behavior, and a number of friends, connections, or contactsassociated with the user, a number of messages sent/received by theuser, a number of phone calls placed/received by the user, a number ofsocial media posts posted by the user, and a number of social mediaposts referencing the user.
 6. The method of claim 1, whereindetermining whether the user is eligible for the identified one or moreproducts comprises: determining that one or more of the one or moresocial scores is above a corresponding threshold value; and in responseto the determination that one or more of the one or more social scoresis above the corresponding threshold value, determining that the user iseligible for the identified one or more products, wherein the outputindicates that the user is eligible for the identified one or moreproducts.
 7. The method of claim 1, wherein determining whether the useris eligible for the identified one or more products comprises:determining that at least one of the one or more social scores is belowa corresponding threshold value; and in response to the determinationthat at least one of the one or more social scores is below thecorresponding threshold value, determining that eligibility of the userfor the identified one or more products is uncertain, wherein the outputindicates that eligibility for the identified one or more products couldnot be determined and additional information is required.
 8. The methodof claim 1, wherein determining whether the user is eligible for theidentified one or more products is further based on results of aninteractive condition evaluation test, wherein the interactive conditionevaluation test includes at least one of a biometric test, a mobilitytest, a reflex test, or a cognitive skills test.
 9. The method of claim1, wherein collecting the social data related to the user furthercomprises: sending, to the user computing device, a instructions tocollect additional social data comprising stored images, a quantity ofcontacts, a quantity of text and email messages sent/received by theuser, a quantity of phone calls placed/received by the user; andreceiving, from the user computing device, the additional social data,wherein the social data further comprises images, email messages, textmessages, quantity of phone calls, or a quantity of contacts.
 10. Acomputing device, comprising: a processing unit comprising a processor;and a memory unit storing computer-executable instructions, which whenexecuted by the processing unit, cause the computing device to: receive,at a communication interface of an eligibility determination computingplatform and from a user computing device, a user input including userinformation for a user, wherein the user input includes sensorinformation; identify, based on the received user information, one ormore products via a product identification module of the eligibilitydetermination computing platform; identify, via a social data analysismodule of the eligibility determination computing platform, one or moresocial networking service accounts associated with the user; collect, bya social data collection module of the eligibility determinationcomputing platform, social data related to the user by scanning the oneor more social networking service accounts associated with the user;analyze, by the social data analysis module of the eligibilitydetermination computing platform and using one or more machine learningdatasets generated using a machine learning engine, the social data toevaluate social habits of the user; determine a quantity of the socialdata falling into each of a plurality of categories of social behavior;calculate, via a social profile generation module of the eligibilitydetermination computing platform and based on the analysis of the socialdata via the one or more machine learning datasets and the quantity ofsocial data falling into each of the plurality of categories of socialbehavior, one or more social scores; determine, based on the one or moresocial scores, whether the user is eligible for the identified one ormore products; and send, via the communication interface and to the usercomputing device, an output indicating whether the use is eligible forthe identified one or more products.
 11. The computing device of claim10, wherein the instructions further cause the computing device toanalyze the social data by: scanning the social data for language orimages indicative of social behavior of the user; and analyzing, usingthe one or more machine learning datasets, the language or images toevaluate the social habits of the user.
 12. The computing device ofclaim 10, wherein the instructions further cause the computing deviceto: calculate the one or more social scores by weighting differentlyeach of the plurality of categories of social behavior.
 13. Thecomputing device of claim 10, wherein the instructions further cause thecomputing device to: determine that one or more of the one or moresocial scores is above a corresponding threshold value; and in responseto the determination that one or more of the one or more social scoresis above the corresponding threshold value, determine that the user iseligible for the identified one or more products, wherein the outputindicates that the user is eligible for the identified one or moreproducts.
 14. The computing device of claim 10, wherein the instructionsfurther cause the computing device to: determine that at least one ofthe one or more social scores is below a corresponding threshold value;and in response to the determination that at least one of the one ormore social scores is below the corresponding threshold value, determinethat eligibility of the user for the identified one or more products isuncertain, wherein the output indicates that eligibility for theidentified one or more products could not be determined and additionalinformation is required.
 15. The computing device of claim 10, whereinthe instructions further cause the computing device to determine whetherthe user is eligible for the identified one or more products, furtherbased on results of an interactive condition evaluation test.
 16. Anon-transitory, computer-readable media storing instructions that, whenexecuted by a computing platform comprising at least one processor,memory, and communication interface, cause the computing platform to:receive, at the communication interface of an eligibility determinationcomputing platform and from a user computing device, a user inputincluding user information for a user; identify, via a productidentification module of the eligibility determination computingplatform and based on the received user information, one or moreproducts; identify, via a social data analysis module of the eligibilitydetermination computing platform, one or more social networking serviceaccounts associated with the user; collect, by a social data collectionmodule of the eligibility determination computing platform, social datarelated to the user by scanning the one or more social networkingservice accounts associated with the user; analyze, by the social dataanalysis module of the eligibility determination computing platform andusing one or more machine learning datasets generated using a machinelearning engine, the social data to evaluate social habits of the user;determine a quantity of the social data falling into each of a pluralityof categories of social behavior; calculate, via a social profilegeneration module of the eligibility determination computing platformand based on the analysis of the social data via the one or more machinelearning datasets and the quantity of social data falling into each ofthe plurality of categories of social behavior, one or more socialscores; determine, based on the one or more social scores, whether theuser is eligible for the identified one or more products; and send, viathe communication interface and to the user computing device, an outputindicating whether the user is eligible for the identified one or moreproducts.
 17. The non-transitory, computer-readable media of claim 16,wherein the instructions further cause the computing platform to analyzethe social data by: scanning the social data for language or imagesindicative of social behavior of the user; and analyzing, using the oneor more machine learning datasets, the language or images to evaluatethe social habits of the user.
 18. The non-transitory, computer-readablemedia of claim 16, wherein the instructions further cause the computingplatform to: calculate the one or more social scores by weightingdifferently each of the plurality of categories of analyzed socialbehavior.
 19. The non-transitory, computer-readable media of claim 16,wherein the instructions further cause the computing platform to:determine that one or more of the one or more social scores is above acorresponding threshold value; and in response to the determination thatone or more of the one or more social scores is above the correspondingthreshold value, determine that the user is eligible for the identifiedone or more products, wherein the output indicates that the user iseligible for the identified one or more products.
 20. Thenon-transitory, computer-readable media of claim 16, wherein theinstructions further cause the computing platform to: determine that atleast one of the one or more social scores is below a correspondingthreshold value; and in response to the determination that at least oneof the one or more social scores is below the corresponding thresholdvalue, determine that eligibility of the user for the identified one ormore products is uncertain, wherein the output indicates thateligibility for the identified one or more products could not bedetermined and additional information is required.